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Positive matrix factorization of organic aerosol: insights from a chemical transport model

机译：有机气溶胶的正矩阵分解：化学传递模型的见解

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Factor analysis of aerosol mass spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR, Particulate Matter Comprehensive Air Quality Model with extensions – source resolved) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is “chemically different” from the others. The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30?% and of the other primary sources less than 40?%, when these sources contribute more than 20?% to the total OA. The PMF uncertainty increases for smaller source contributions, reaching a factor of 2 or even 3 for sources which contribute less than 10?% to the OA. One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low- and high-volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as a low-volatility factor is indeed lower than that of the other (high-volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher secondary organic aerosol from volatile, semivolatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.
机译：气溶胶质谱仪测量的因子分析（有机气溶胶质谱）通常用于确定有机气溶胶（OA）的来源。在这项研究中，我们旨在获得有关正矩阵分解（PMF）准确识别和量化OA来源的能力的见解。我们在光化学活跃期间对最先进的化学传输模型（PMCAMx-SR，带有扩展的微粒物质综合空气质量模型-分解源）的预测进行了PMF和多线性引擎（ME-2）分析。欧洲特定站点，以解释通常通过PMF现场测量分析确定的各种因素。我们的分析使用了27种OA成分的预测浓度，并假设每种成分在化学上均互不相同。基于化学迁移模型预测的PMF结果与AMS测量分析的结果非常一致（相同数量的因子和源类型）。当新鲜生物质燃烧占总OA的比例超过20％时，估计的不确定性是新鲜生物质燃烧的贡献度小于30％，其他主要来源的不确定度小于40％。对于较小的源贡献，PMF不确定性增加，对于对OA贡献不到10％的源，PMF不确定性达到2甚至3倍。 PMF对AMS测量的分析中的主要问题之一涉及在田野研究中经常报告的两个或多个氧化OA（OOA）因子的来源。我们的分析表明，这些因素包括来自各种人为和生物来源的次生OA化合物，并不对应于特定来源。在文献中将它们描述为低波动率因素和高波动率因素可能会产生误导，因为它们具有重叠的波动率分布。但是，一个通常被称为低波动率因子的平均波动率确实低于另一个波动率（高波动率因子）。根据对PMCAMx-SR预测的分析，第一个含氧OA因子主要包括从欧洲以外地区运输的高龄OA，但也包括来自欧洲排放的前体的高龄次生OA。第二个氧化的OA因子包含来自挥发性，半挥发性和中等挥发性的人为和生物成因有机化合物的较新鲜的二次有机气溶胶。这些OA成分对每个OA因子的确切贡献取决于分析期间的站点和主要的气象。

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